I used it for non ML tasks in geophysics, which made my life a lot easier. However, I think most scientists and engineers aren't aware of it. It has been described as "criminally underused."
I'm curious - how do you know it's used for most industries? I'm sure there are lots of industries that use ML, and lots that don't. Is there a report on this somewhere? I just get a lot of worthless hype articles when I google it.
For sure! I've managed to get a good chunk of people I work/coauthor with to use it (and like it!), and I've observed an increasing number of my students using it as well (in classes on ML and numerical computing).
Yes it really is underappreciated. As quoted by other comments, "Most businesses think they need advanced ML and really what they need is linear regression and cleaned up data". A significant portion of businesses currently investing millions in ML should basically hire a couple of statisticians and get over it.
Yes! It's one tool in the software engineering toolbox! It's a great tool for some problems!
Due to the hype it becomes a goal in some organizations however. "We need to do machine learning because we have big data" or some such. Doesn't matter if the problem could've been solved in 5% of the time and cost with 20 lines of code, thou shalt use machine learning.
It doesn't help that data scientists (creating and training the ML model) and software developers (creating and maintaining the software) usually come from different backgrounds, requiring a "data engineer" as an additional intermediary.
It always a problem with hype, blockchain (or merkle trees) has the same problem but worse, because the problems it solves well are rarer and more narrow.
Its useful for ML in that it requires so much optimization, that being about to drop down in the same language is incredibly handy. For ML software isn't lagging hardware its the other way around.
exactly. Im not saying ML should be used in more places. I'm saying that there's almost never a real need for it, despite how hyped it can be and how cool the math/algorithms are in theory
IMHO, ML people are pretty pragmatic, possible due to the fact ML itself a mixed paradigm with people from different background, so they don't hold religious belief towards programming languages comparing to some pure CS background folks.
We use DL at my company. It's used to help and suggest the people in data input what some of the inputs should be. It's been getting better over time as it collects more data points.
Apart from that, I'm also personally responsible for implementing DL for data anomaly detection.
So yes, ML/DL is very much over-hyped right now, but it has real world tangible benefits. It is being used in multiple fields and I believe will grow quite fast.
ML is a general-purpose programming language, and as such its use case cannot be narrowly specified. (They say it's good for writing compilers, but I wouldn't latch on that.)
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